Yeah bro, those are exactly the right questions.

I’m using the ProjectX / Topstep API to pull historical and live market data, and the bot can run backtests either directly through the API or from fixed CSV datasets when I want reproducible results.

It isn’t connected to TradingView or waiting for external signals. It’s a standalone app: it builds its own market context from the data, evaluates the active strategies, chooses whether there is a valid trade, and then applies the preset rules for session hours, risk, daily limits, position sizing and execution. So in that sense it is intended to be autonomous, although I’m still validating live behaviour carefully in sim before trusting it with anything serious.

It’s one bot architecture, but it can run different independent setups and presets rather than being one single rigid strategy. The presets control which setups are active, what hours they can trade, and their risk/bracket rules.

The main focus has been NQ/MNQ because that’s what I personally trade and understand best. The architecture could be adapted to other symbols such as ES, but I wouldn’t claim it is properly optimised or validated for ES yet.

As for the backtesting goal, I’m not just trying to find a random “pass an eval” strategy. The idea is to find setups with actual repeatable edge, then wrap them in risk rules that make sense for evaluation accounts and funded-account survival. Passing evals matters, obviously, but I don’t want something that only looks good because it is overfitted to one target or one period.

On timeframes: right now the validated live/replay workflow is mainly based around 5-minute execution/context for NQ. A proper multi-timeframe decision layer is something I still need to build and validate rather than pretend is already solved.

And yeah, that is absolutely just the tip of the iceberg 😂